Project Gutenberg: Language Patterns in Author Longevity

Comparing lifespans of English vs non-English authors across centuries

TidyTuesday
Data Visualization
R Programming
2025
Exploring author longevity patterns in Project Gutenberg’s digital library through advanced data visualization. This TidyTuesday analysis reveals how English-language publishing dominated the 19th century while uncovering surprising differences in author lifespans across languages and time periods using beeswarm plots and statistical analysis.
Author

Steven Ponce

Published

June 3, 2025

Figure 1: Beeswarm plot comparing lifespans of English vs non-English authors across three centuries for Project Gutenberg. Each dot represents one author, with blue dots for English authors and pink for other languages. Shows dramatic increase in English-language authors in the 19th century, with relatively consistent longevity patterns except for 20th century where English authors lived longer on average.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,     # Easily Install and Load the 'Tidyverse'
  ggtext,        # Improved Text Rendering Support for 'ggplot2'
  showtext,      # Using Fonts More Easily in R Graphs
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  scales,        # Scale Functions for Visualization
  glue,          # Interpreted String Literals
  ggbeeswarm     # Categorical Scatter (Violin Point) Plots
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  8,
  height =  8,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 22)

authors_raw <- tt$gutenberg_authors |> clean_names()
languages_raw <- tt$gutenberg_languages |> clean_names()
metadata_raw <- tt$gutenberg_metadata |> clean_names()
subjects_raw <- tt$gutenberg_subjects |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(authors_raw)
skimr::skim(authors_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

### |-  tidy data ----
# Preprocessing
authors_clean <- authors_raw |>
  mutate(
    lifespan = deathdate - birthdate,
    birth_century = case_when(
      birthdate >= 1800 & birthdate < 1900 ~ "19th Century",
      birthdate >= 1700 & birthdate < 1800 ~ "18th Century",
      birthdate >= 1900 & birthdate < 2000 ~ "20th Century",
      birthdate < 1700 ~ "Pre-18th Century",
      TRUE ~ "Unknown"
    )
  )

# Join datasets
full_data <- metadata_raw |>
  left_join(languages_raw, by = "gutenberg_id") |>
  left_join(authors_clean, by = "gutenberg_author_id")

# Beeswarm data
beeswarm_data <- full_data |>
  select(lifespan, birth_century, language.x, author.x) |>
  rename(language = language.x, author = author.x) |>
  filter(
    !is.na(lifespan), lifespan > 0, lifespan < 120,
    !is.na(birth_century), birth_century != "Unknown"
  ) |>
  mutate(
    highlight = ifelse(language == "en", "English", "Other Languages"),
    birth_century = factor(birth_century,
      levels = c(
        "Pre-18th Century", "18th Century",
        "19th Century", "20th Century"
      )
    )
  )

# Stats for annotations
detailed_stats <- beeswarm_data |>
  filter(birth_century %in% c("18th Century", "19th Century", "20th Century")) |>
  group_by(birth_century, highlight) |>
  summarise(
    median_lifespan = median(lifespan, na.rm = TRUE),
    count = n(),
    mean_lifespan = round(mean(lifespan, na.rm = TRUE), 1),
    .groups = "drop"
  )
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "English" = "#2E86AB", "Other Languages" = "#A23B72"
    )
)

### |-  titles and caption ----
title_text <- str_glue("Project Gutenberg: Language Patterns in Author Longevity")

subtitle_text <- str_glue("Comparing lifespans of English vs non-English authors across centuries\n",
                          "n = sample size • μ = mean lifespan • Boxes show median and quartiles")

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 22,
  source_text =  "The R gutenbergr package"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),
    
    # Grid elements
    panel.grid.minor.x = element_line(color = 'gray50', linewidth = 0.05),
    panel.grid.major.x = element_blank(), #element_line(color = 'gray50', linewidth = 0.1),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),

    # Plot margins
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

# Final plot -----
p <- beeswarm_data |>
    filter(birth_century %in% c("18th Century", "19th Century", "20th Century")) |>
    ggplot(aes(x = highlight, y = lifespan, color = highlight)) +
    # Geoms
    geom_quasirandom(alpha = 0.5, size = 0.7) +
    geom_boxplot(alpha = 0.3, outlier.shape = NA, width = 0.3) +
    geom_text(data = detailed_stats, 
              aes(x = highlight, y = 112, 
                  label = paste0("n=", scales::comma(count), "\nμ=", mean_lifespan)),
              color = "gray20", size = 2.8, inherit.aes = FALSE, 
              vjust = 1, fontface = "bold") +
    # Scales
    scale_color_manual(values = colors$palette) +
    scale_y_continuous(breaks = seq(20, 100, 20), limits = c(15, 115)) +
    # Facets
    facet_wrap(~birth_century, scales = "free_x") +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = "Language Group",
        y = "Lifespan (Years)",
    ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.55),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.75),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    strip.text = element_text(size = rel(0.78), face = "bold"),
    panel.spacing = unit(1, "lines"),
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 22, 
  width = 8,
  height = 8
)
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1       ggbeeswarm_0.7.2 glue_1.8.0       scales_1.3.0    
 [5] janitor_2.2.0    showtext_0.9-7   showtextdb_3.0   sysfonts_0.8.9  
 [9] ggtext_0.1.2     lubridate_1.9.3  forcats_1.0.0    stringr_1.5.1   
[13] dplyr_1.1.4      purrr_1.0.2      readr_2.1.5      tidyr_1.3.1     
[17] tibble_3.2.1     ggplot2_3.5.1    tidyverse_2.0.0  pacman_0.5.1    

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   vipor_0.4.7        farver_2.1.2       fastmap_1.2.0     
 [5] gh_1.4.1           digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6       
[13] tools_4.4.0        utf8_1.2.4         yaml_2.3.10        knitr_1.49        
[17] skimr_2.1.5        htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0        
[21] xml2_1.3.6         camcorder_0.1.0    repr_1.1.7         tidytuesdayR_1.1.2
[25] withr_3.0.2        grid_4.4.0         fansi_1.0.6        colorspace_2.1-1  
[29] gitcreds_0.1.2     cli_3.6.4          rmarkdown_2.29     crayon_1.5.3      
[33] ragg_1.3.3         generics_0.1.3     rstudioapi_0.17.1  tzdb_0.5.0        
[37] commonmark_1.9.2   parallel_4.4.0     base64enc_0.1-3    vctrs_0.6.5       
[41] jsonlite_1.8.9     hms_1.1.3          bit64_4.5.2        beeswarm_0.4.0    
[45] systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6       
[57] textshaping_0.4.0  rprojroot_2.0.4    vroom_1.6.5        evaluate_1.0.1    
[61] markdown_1.13      gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[65] Rcpp_1.0.13-1      svglite_2.1.3      xfun_0.49          pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_22.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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